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基于Elman神经网络的地层水污染率近红外光谱实时测量方法
投稿时间:2022-08-30  修订日期:2022-09-08  点此下载全文
引用本文:孔笋,沈阳,左有祥,褚晓冬.基于Elman神经网络的地层水污染率近红外光谱实时测量方法[J].红外,2022,43(12):37~44
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作者单位E-mail
孔笋 中海油田服务股份有限公司油田技术研究院 ks66_lulu@126.com 
沈阳 中海油田服务股份有限公司油田技术研究院  
左有祥 中海油田服务股份有限公司油田技术研究院  
褚晓冬 中海油田服务股份有限公司油田技术研究院  
基金项目:中海油集团公司重大科技专项(YJB22YF003)
中文摘要:在钻井过程中,水基钻井泥浆会透过泥饼渗入地层从而污染地层水。为了取得纯净的地层水样品,需要对其污染程度进行实时监测。取样前,流体由水基泥浆滤液逐渐过渡到纯净地层水,通过获取滤液、纯地层水以及混合流体的吸光度可实时计算地层水受到污染的程度。鉴于井下地层水污染率在线监测可看作时间序列预测问题,采用了Elman神经网络模型对吸光度数据进行训练,从而预测纯地层水吸光度。采用海上实井数据进行了验证,将基于Elman神经网络预测得到的地层水吸光度与泵抽初期采集的钻井液泥浆滤液吸光度相结合,可以计算出实时的地层水污染率,并将其与实验室水分析结果进行了对比。结果表明,它们的一致性很好。与传统算法相比,新方法高效可靠,具有广泛的适用性和较好的应用价值。
中文关键词:水基泥浆滤液  地层水  污染监测:近红外  Elman神经网络
 
Real Time Measurement of Formation Water Pollution Rate by Near-Infrared Spectrum Based on Elman Neural Network
Abstract:During the drilling process, water based drilling mud can infiltrate the formation through mud cake and contaminate the formation water. In order to obtain pure formation water samples, it is necessary to monitor the pollution level in real time. Before sampling, the fluid is gradually transferred from water-based mud filtrate to pure formation water. The degree of formation water contamination can be calculated in real-time by obtaining the absorbance of filtrate, pure formation water, and mixed fluid. In view of the fact that online monitoring of downhole formation water pollution rate can be regarded as a time series prediction problem, Elman neural network model is adopted to train the absorbance data, so as to predict the absorbance of pure formation water. The validation is performed using offshore well data. Combining the absorbance of the formation water based on the Elman neural network and that of the drilling fluid filtrate collected at the initial pumping stage, the real-time formation water pollution rate can be calculated and compared with the laboratory water analysis results. The results show that they are in good agreement. Compared with the traditional algorithm, the new method is efficient and reliable, and has wide applicability and good application value.
keywords:water-based mud filtrate  formation water  pollution monitoring: near-infrared  Elman neural network
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